A trait becoming increasingly common in computer systems is their ability to measure the affective response of their users. From the affective response measurements, which include measurements of physiological signals and/or behavioral cues, the computer systems can deduce how users feel and gain insight towards the emotional responses the users express. This ability paves the way to many new affective computing applications that rely on the information about the users' emotional state in order to deliver content that is more relevant to users and to tailor services to improve the users' experience.
A user's typical interaction with computer systems involves receiving content many times a day through various channels. Some examples of content users are likely to be exposed to include communications with other users (e.g., video conversations and instant messages), communications with a computer (e.g., interaction with a user's virtual agent), and/or various forms of digital media (e.g., internet sites, television shows, movies, and/or interactive computer games). Throughout these interactions, it can be beneficial for the computer systems to measure the user's affective response in order to gain valuable information on how the user feels.
Computer systems can utilize a wide array of sensors to measure users' affective response signals. For example, cameras and microphones are able to track a user's gestures, facial expressions and/or voice. In addition, there are various types of physiological sensors that can measure physiological signals such as heart rate, blood-volume pulse, galvanic skin response (GSR), skin temperature, respiration, or brainwave activity such as electroencephalography (EEG). These sensors come in many forms, and can be attached to, or embedded in, devices, clothing, and even implanted in the human body.
In the modern world, users are practically interacting with computer systems all day long. This can result in users having their affective response measured for long periods; consequently, copious amounts of data may be generated. This raises several issues concerning the computational resources a computer system may have to consume in order to process and/or transmit the collected data. For example, sensors like cameras or EEG sensors often produce many high dimensional data points. Processing this data, such as filtering, analyzing, extracting features, compressing, and/or encrypting can require a system to perform a significant amount of computations. In addition, sensor measurement data is often collected by mobile battery powered devices, and processing and/or transmitting the data is also performed, at least in part, on mobile devices. Therefore, handling the measurement data can involve an expenditure of energy from the mobile devices' limited energy supplies.
Thus, there is a need to reduce the computational load involved in processing data that includes measurements of users' affective response signals. Unchecked expenditure of computational resources may deplete system resources and/or reduce the systems' ability to operate effectively when needed; this can ultimately end up reducing the quality of information and/or services the computer systems are able to provide their users.